The effects of spatial constraints on the evolution of weighted complex networks

By Alain Barrat, Marc Barthelemy, Alessandro Vespignani. 2005

197 citations recorded by the source corpus.

Motivated by the empirical analysis of the air transportation system, we define a network model that includes geographical attributes along with topological and weight (traffic) properties. The introduction of geographical attributes is made by constraining the network in real space. Interestingly, the inclusion of geometrical features induces non-trivial correlations between the weights, the connectivity pattern and the actual spatial distances of vertices. The model also recovers the emergence of anomalous fluctuations in the betweenness-degree correlation function as first observed by Guimerà and Amaral (2004 Eur. Phys. J. B 38 381). The presented results suggest that the interplay between weight dynamics and spatial constraints is a key ingredient in order to understand the formation of real-world weighted networks.

Subfields: Building and Construction, Statistical and Nonlinear Physics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/60ee276fcb9c1b1540cfa69023dc9e7c5e8968f0

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Citations in the corpus

Spatial Science and Network Science: Review and Outcomes of a Complex Relationship (2014, background): “Further results were provided by Barrat et al. (2005) based on the case of air transport highlighting several other properties of spatial networks such as: fewer global hubs and more regional hubs, higher disassortativity as the network grows (nodes having a high degree tend to connect…”
Exploring the structure of the U.S. intercity passenger air transportation network: a weighted complex network approach (2008, background): “Afterwards, the order number of the first vertex entering the model is 11. At each time step, a vertex will be added to the network model according to their order number in Fig. 11 and two evolutionary mechanisms will be simulated: (1) A rule termed spatially constrained strength driven attachment (”
Exploring the network structure and nodal centrality of China (2011, background): “For the air transport network of the US, when k < 30, the network is assortative; when k P 30, it becomes disassortative (Barrat et al., 2005).”
Analysis of fungal networks (2012, methodology): “…reduction, using median or band-pass filters, intensity-based thresholding, with manual or automated threshold selection, followed by morphological processing to achieve an improved skeletonisation of the colony structure e.g. (Tucker et al., 1992; Barry et al., 2009; Barry and Williams, 2011).”